Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations30000
Missing cells600
Missing cells (%)0.1%
Duplicate rows32
Duplicate rows (%)0.1%
Total size in memory5.5 MiB
Average record size in memory192.0 B

Variable types

Numeric14
Categorical10

Alerts

Dataset has 32 (0.1%) duplicate rowsDuplicates
bill_statement_apr is highly overall correlated with bill_statement_aug and 8 other fieldsHigh correlation
bill_statement_aug is highly overall correlated with bill_statement_apr and 5 other fieldsHigh correlation
bill_statement_jul is highly overall correlated with bill_statement_apr and 6 other fieldsHigh correlation
bill_statement_jun is highly overall correlated with bill_statement_apr and 9 other fieldsHigh correlation
bill_statement_may is highly overall correlated with bill_statement_apr and 9 other fieldsHigh correlation
bill_statement_sep is highly overall correlated with bill_statement_apr and 5 other fieldsHigh correlation
payment_status_apr is highly overall correlated with payment_status_jun and 1 other fieldsHigh correlation
payment_status_aug is highly overall correlated with payment_status_jul and 1 other fieldsHigh correlation
payment_status_jul is highly overall correlated with payment_status_aug and 1 other fieldsHigh correlation
payment_status_jun is highly overall correlated with payment_status_apr and 2 other fieldsHigh correlation
payment_status_may is highly overall correlated with payment_status_apr and 1 other fieldsHigh correlation
payment_status_sep is highly overall correlated with payment_status_augHigh correlation
previous_payment_apr is highly overall correlated with bill_statement_apr and 4 other fieldsHigh correlation
previous_payment_aug is highly overall correlated with bill_statement_jul and 5 other fieldsHigh correlation
previous_payment_jul is highly overall correlated with bill_statement_apr and 7 other fieldsHigh correlation
previous_payment_jun is highly overall correlated with bill_statement_apr and 6 other fieldsHigh correlation
previous_payment_may is highly overall correlated with bill_statement_apr and 5 other fieldsHigh correlation
previous_payment_sep is highly overall correlated with bill_statement_aug and 5 other fieldsHigh correlation
payment_status_aug is highly imbalanced (58.4%) Imbalance
payment_status_jul is highly imbalanced (59.7%) Imbalance
payment_status_jun is highly imbalanced (62.1%) Imbalance
payment_status_may is highly imbalanced (62.3%) Imbalance
payment_status_apr is highly imbalanced (61.7%) Imbalance
previous_payment_aug is highly skewed (γ1 = 30.45381745) Skewed
bill_statement_sep has 2008 (6.7%) zeros Zeros
bill_statement_aug has 2506 (8.4%) zeros Zeros
bill_statement_jul has 2870 (9.6%) zeros Zeros
bill_statement_jun has 3195 (10.7%) zeros Zeros
bill_statement_may has 3506 (11.7%) zeros Zeros
bill_statement_apr has 4020 (13.4%) zeros Zeros
previous_payment_sep has 5249 (17.5%) zeros Zeros
previous_payment_aug has 5396 (18.0%) zeros Zeros
previous_payment_jul has 5968 (19.9%) zeros Zeros
previous_payment_jun has 6408 (21.4%) zeros Zeros
previous_payment_may has 6703 (22.3%) zeros Zeros
previous_payment_apr has 7173 (23.9%) zeros Zeros

Reproduction

Analysis started2024-11-04 01:13:54.786548
Analysis finished2024-11-04 01:14:13.939818
Duration19.15 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

limit_bal
Real number (ℝ)

Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167484.32
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2024-11-04T10:14:14.004271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129747.66
Coefficient of variation (CV)0.77468541
Kurtosis0.5362629
Mean167484.32
Median Absolute Deviation (MAD)90000
Skewness0.99286696
Sum5.0245297 × 109
Variance1.6834456 × 1010
MonotonicityNot monotonic
2024-11-04T10:14:14.112122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 3365
 
11.2%
20000 1976
 
6.6%
30000 1610
 
5.4%
80000 1567
 
5.2%
200000 1528
 
5.1%
150000 1110
 
3.7%
100000 1048
 
3.5%
180000 995
 
3.3%
360000 881
 
2.9%
60000 825
 
2.8%
Other values (71) 15095
50.3%
ValueCountFrequency (%)
10000 493
 
1.6%
16000 2
 
< 0.1%
20000 1976
6.6%
30000 1610
5.4%
40000 230
 
0.8%
50000 3365
11.2%
60000 825
 
2.8%
70000 731
 
2.4%
80000 1567
5.2%
90000 651
 
2.2%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
800000 2
 
< 0.1%
780000 2
 
< 0.1%
760000 1
 
< 0.1%
750000 4
< 0.1%
740000 2
 
< 0.1%
730000 2
 
< 0.1%
720000 3
 
< 0.1%
710000 6
< 0.1%
700000 8
< 0.1%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing150
Missing (%)0.5%
Memory size234.5 KiB
Female
18027 
Male
11823 

Length

Max length6
Median length6
Mean length5.2078392
Min length4

Characters and Unicode

Total characters155454
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 18027
60.1%
Male 11823
39.4%
(Missing) 150
 
0.5%

Length

2024-11-04T10:14:14.219906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T10:14:14.307096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
female 18027
60.4%
male 11823
39.6%

Most occurring characters

ValueCountFrequency (%)
e 47877
30.8%
a 29850
19.2%
l 29850
19.2%
F 18027
 
11.6%
m 18027
 
11.6%
M 11823
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 155454
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 47877
30.8%
a 29850
19.2%
l 29850
19.2%
F 18027
 
11.6%
m 18027
 
11.6%
M 11823
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 155454
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 47877
30.8%
a 29850
19.2%
l 29850
19.2%
F 18027
 
11.6%
m 18027
 
11.6%
M 11823
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 155454
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 47877
30.8%
a 29850
19.2%
l 29850
19.2%
F 18027
 
11.6%
m 18027
 
11.6%
M 11823
 
7.6%

education
Categorical

Distinct4
Distinct (%)< 0.1%
Missing150
Missing (%)0.5%
Memory size234.5 KiB
University
13960 
Graduate school
10537 
High school
4886 
Others
 
467

Length

Max length15
Median length11
Mean length11.866097
Min length6

Characters and Unicode

Total characters354203
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUniversity
2nd rowUniversity
3rd rowUniversity
4th rowUniversity
5th rowUniversity

Common Values

ValueCountFrequency (%)
University 13960
46.5%
Graduate school 10537
35.1%
High school 4886
 
16.3%
Others 467
 
1.6%
(Missing) 150
 
0.5%

Length

2024-11-04T10:14:14.386272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T10:14:14.461625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
school 15423
34.1%
university 13960
30.8%
graduate 10537
23.3%
high 4886
 
10.8%
others 467
 
1.0%

Most occurring characters

ValueCountFrequency (%)
i 32806
 
9.3%
o 30846
 
8.7%
s 29850
 
8.4%
e 24964
 
7.0%
t 24964
 
7.0%
r 24964
 
7.0%
a 21074
 
5.9%
h 20776
 
5.9%
15423
 
4.4%
l 15423
 
4.4%
Other values (11) 113113
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 354203
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 32806
 
9.3%
o 30846
 
8.7%
s 29850
 
8.4%
e 24964
 
7.0%
t 24964
 
7.0%
r 24964
 
7.0%
a 21074
 
5.9%
h 20776
 
5.9%
15423
 
4.4%
l 15423
 
4.4%
Other values (11) 113113
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 354203
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 32806
 
9.3%
o 30846
 
8.7%
s 29850
 
8.4%
e 24964
 
7.0%
t 24964
 
7.0%
r 24964
 
7.0%
a 21074
 
5.9%
h 20776
 
5.9%
15423
 
4.4%
l 15423
 
4.4%
Other values (11) 113113
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 354203
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 32806
 
9.3%
o 30846
 
8.7%
s 29850
 
8.4%
e 24964
 
7.0%
t 24964
 
7.0%
r 24964
 
7.0%
a 21074
 
5.9%
h 20776
 
5.9%
15423
 
4.4%
l 15423
 
4.4%
Other values (11) 113113
31.9%

marriage
Categorical

Distinct3
Distinct (%)< 0.1%
Missing150
Missing (%)0.5%
Memory size234.5 KiB
Single
15891 
Married
13585 
Others
 
374

Length

Max length7
Median length6
Mean length6.4551089
Min length6

Characters and Unicode

Total characters192685
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowSingle
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Single 15891
53.0%
Married 13585
45.3%
Others 374
 
1.2%
(Missing) 150
 
0.5%

Length

2024-11-04T10:14:14.548739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T10:14:14.622338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
single 15891
53.2%
married 13585
45.5%
others 374
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e 29850
15.5%
i 29476
15.3%
r 27544
14.3%
S 15891
8.2%
g 15891
8.2%
n 15891
8.2%
l 15891
8.2%
M 13585
7.1%
a 13585
7.1%
d 13585
7.1%
Other values (4) 1496
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 192685
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 29850
15.5%
i 29476
15.3%
r 27544
14.3%
S 15891
8.2%
g 15891
8.2%
n 15891
8.2%
l 15891
8.2%
M 13585
7.1%
a 13585
7.1%
d 13585
7.1%
Other values (4) 1496
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 192685
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 29850
15.5%
i 29476
15.3%
r 27544
14.3%
S 15891
8.2%
g 15891
8.2%
n 15891
8.2%
l 15891
8.2%
M 13585
7.1%
a 13585
7.1%
d 13585
7.1%
Other values (4) 1496
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 192685
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 29850
15.5%
i 29476
15.3%
r 27544
14.3%
S 15891
8.2%
g 15891
8.2%
n 15891
8.2%
l 15891
8.2%
M 13585
7.1%
a 13585
7.1%
d 13585
7.1%
Other values (4) 1496
 
0.8%

age
Real number (ℝ)

Distinct56
Distinct (%)0.2%
Missing150
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean35.490117
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2024-11-04T10:14:14.706913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.217852
Coefficient of variation (CV)0.25973011
Kurtosis0.046425463
Mean35.490117
Median Absolute Deviation (MAD)6
Skewness0.73247263
Sum1059380
Variance84.968796
MonotonicityNot monotonic
2024-11-04T10:14:14.812289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 1602
 
5.3%
27 1470
 
4.9%
28 1402
 
4.7%
30 1388
 
4.6%
26 1246
 
4.2%
31 1208
 
4.0%
25 1180
 
3.9%
32 1156
 
3.9%
34 1154
 
3.8%
33 1139
 
3.8%
Other values (46) 16905
56.4%
ValueCountFrequency (%)
21 66
 
0.2%
22 558
 
1.9%
23 922
3.1%
24 1120
3.7%
25 1180
3.9%
26 1246
4.2%
27 1470
4.9%
28 1402
4.7%
29 1602
5.3%
30 1388
4.6%
ValueCountFrequency (%)
79 1
 
< 0.1%
75 3
 
< 0.1%
74 1
 
< 0.1%
73 4
 
< 0.1%
72 3
 
< 0.1%
71 3
 
< 0.1%
70 10
< 0.1%
69 15
0.1%
68 5
 
< 0.1%
67 16
0.1%

payment_status_sep
Categorical

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
Unknown
17496 
Payed duly
5686 
Payment delayed 1 month
3688 
Payment delayed 2 months
2667 
Payment delayed 3 months
 
322
Other values (5)
 
141

Length

Max length24
Median length7
Mean length11.3092
Min length7

Characters and Unicode

Total characters339276
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayment delayed 2 months
2nd rowPayed duly
3rd rowUnknown
4th rowUnknown
5th rowPayed duly

Common Values

ValueCountFrequency (%)
Unknown 17496
58.3%
Payed duly 5686
 
19.0%
Payment delayed 1 month 3688
 
12.3%
Payment delayed 2 months 2667
 
8.9%
Payment delayed 3 months 322
 
1.1%
Payment delayed 4 months 76
 
0.3%
Payment delayed 5 months 26
 
0.1%
Payment delayed 8 months 19
 
0.1%
Payment delayed 6 months 11
 
< 0.1%
Payment delayed 7 months 9
 
< 0.1%

Length

2024-11-04T10:14:14.913147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T10:14:15.006695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 17496
31.2%
delayed 6818
 
12.1%
payment 6818
 
12.1%
duly 5686
 
10.1%
payed 5686
 
10.1%
1 3688
 
6.6%
month 3688
 
6.6%
months 3130
 
5.6%
2 2667
 
4.8%
3 322
 
0.6%
Other values (5) 141
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n 66124
19.5%
e 26140
 
7.7%
26140
 
7.7%
d 25008
 
7.4%
y 25008
 
7.4%
o 24314
 
7.2%
a 19322
 
5.7%
U 17496
 
5.2%
k 17496
 
5.2%
w 17496
 
5.2%
Other values (15) 74732
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 339276
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 66124
19.5%
e 26140
 
7.7%
26140
 
7.7%
d 25008
 
7.4%
y 25008
 
7.4%
o 24314
 
7.2%
a 19322
 
5.7%
U 17496
 
5.2%
k 17496
 
5.2%
w 17496
 
5.2%
Other values (15) 74732
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 339276
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 66124
19.5%
e 26140
 
7.7%
26140
 
7.7%
d 25008
 
7.4%
y 25008
 
7.4%
o 24314
 
7.2%
a 19322
 
5.7%
U 17496
 
5.2%
k 17496
 
5.2%
w 17496
 
5.2%
Other values (15) 74732
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 339276
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 66124
19.5%
e 26140
 
7.7%
26140
 
7.7%
d 25008
 
7.4%
y 25008
 
7.4%
o 24314
 
7.2%
a 19322
 
5.7%
U 17496
 
5.2%
k 17496
 
5.2%
w 17496
 
5.2%
Other values (15) 74732
22.0%

payment_status_aug
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
Unknown
19512 
Payed duly
6050 
Payment delayed 2 months
3927 
Payment delayed 3 months
 
326
Payment delayed 4 months
 
99
Other values (5)
 
86

Length

Max length24
Median length7
Mean length10.118933
Min length7

Characters and Unicode

Total characters303568
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPayment delayed 2 months
2nd rowPayment delayed 2 months
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown 19512
65.0%
Payed duly 6050
 
20.2%
Payment delayed 2 months 3927
 
13.1%
Payment delayed 3 months 326
 
1.1%
Payment delayed 4 months 99
 
0.3%
Payment delayed 1 month 28
 
0.1%
Payment delayed 5 months 25
 
0.1%
Payment delayed 7 months 20
 
0.1%
Payment delayed 6 months 12
 
< 0.1%
Payment delayed 8 months 1
 
< 0.1%

Length

2024-11-04T10:14:15.123262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T10:14:15.215767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 19512
39.5%
payed 6050
 
12.3%
duly 6050
 
12.3%
payment 4438
 
9.0%
delayed 4438
 
9.0%
months 4410
 
8.9%
2 3927
 
8.0%
3 326
 
0.7%
4 99
 
0.2%
1 28
 
0.1%
Other values (5) 86
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n 67412
22.2%
o 23950
 
7.9%
y 20976
 
6.9%
d 20976
 
6.9%
U 19512
 
6.4%
w 19512
 
6.4%
k 19512
 
6.4%
19364
 
6.4%
e 19364
 
6.4%
a 14926
 
4.9%
Other values (15) 58064
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 303568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 67412
22.2%
o 23950
 
7.9%
y 20976
 
6.9%
d 20976
 
6.9%
U 19512
 
6.4%
w 19512
 
6.4%
k 19512
 
6.4%
19364
 
6.4%
e 19364
 
6.4%
a 14926
 
4.9%
Other values (15) 58064
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 303568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 67412
22.2%
o 23950
 
7.9%
y 20976
 
6.9%
d 20976
 
6.9%
U 19512
 
6.4%
w 19512
 
6.4%
k 19512
 
6.4%
19364
 
6.4%
e 19364
 
6.4%
a 14926
 
4.9%
Other values (15) 58064
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 303568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 67412
22.2%
o 23950
 
7.9%
y 20976
 
6.9%
d 20976
 
6.9%
U 19512
 
6.4%
w 19512
 
6.4%
k 19512
 
6.4%
19364
 
6.4%
e 19364
 
6.4%
a 14926
 
4.9%
Other values (15) 58064
19.1%

payment_status_jul
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
Unknown
19849 
Payed duly
5938 
Payment delayed 2 months
3819 
Payment delayed 3 months
 
240
Payment delayed 4 months
 
76
Other values (5)
 
78

Length

Max length24
Median length7
Mean length9.9810333
Min length7

Characters and Unicode

Total characters299431
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayed duly
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowPayed duly

Common Values

ValueCountFrequency (%)
Unknown 19849
66.2%
Payed duly 5938
 
19.8%
Payment delayed 2 months 3819
 
12.7%
Payment delayed 3 months 240
 
0.8%
Payment delayed 4 months 76
 
0.3%
Payment delayed 7 months 27
 
0.1%
Payment delayed 6 months 23
 
0.1%
Payment delayed 5 months 21
 
0.1%
Payment delayed 1 month 4
 
< 0.1%
Payment delayed 8 months 3
 
< 0.1%

Length

2024-11-04T10:14:15.331743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T10:14:15.424248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 19849
40.9%
payed 5938
 
12.2%
duly 5938
 
12.2%
payment 4213
 
8.7%
delayed 4213
 
8.7%
months 4209
 
8.7%
2 3819
 
7.9%
3 240
 
0.5%
4 76
 
0.2%
7 27
 
0.1%
Other values (5) 55
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 67973
22.7%
o 24062
 
8.0%
y 20302
 
6.8%
d 20302
 
6.8%
U 19849
 
6.6%
w 19849
 
6.6%
k 19849
 
6.6%
18577
 
6.2%
e 18577
 
6.2%
a 14364
 
4.8%
Other values (15) 55727
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 299431
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 67973
22.7%
o 24062
 
8.0%
y 20302
 
6.8%
d 20302
 
6.8%
U 19849
 
6.6%
w 19849
 
6.6%
k 19849
 
6.6%
18577
 
6.2%
e 18577
 
6.2%
a 14364
 
4.8%
Other values (15) 55727
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 299431
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 67973
22.7%
o 24062
 
8.0%
y 20302
 
6.8%
d 20302
 
6.8%
U 19849
 
6.6%
w 19849
 
6.6%
k 19849
 
6.6%
18577
 
6.2%
e 18577
 
6.2%
a 14364
 
4.8%
Other values (15) 55727
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 299431
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 67973
22.7%
o 24062
 
8.0%
y 20302
 
6.8%
d 20302
 
6.8%
U 19849
 
6.6%
w 19849
 
6.6%
k 19849
 
6.6%
18577
 
6.2%
e 18577
 
6.2%
a 14364
 
4.8%
Other values (15) 55727
18.6%

payment_status_jun
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
Unknown
20803 
Payed duly
5687 
Payment delayed 2 months
3159 
Payment delayed 3 months
 
180
Payment delayed 4 months
 
69
Other values (5)
 
102

Length

Max length24
Median length7
Mean length9.5576333
Min length7

Characters and Unicode

Total characters286729
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayed duly
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown 20803
69.3%
Payed duly 5687
 
19.0%
Payment delayed 2 months 3159
 
10.5%
Payment delayed 3 months 180
 
0.6%
Payment delayed 4 months 69
 
0.2%
Payment delayed 7 months 58
 
0.2%
Payment delayed 5 months 35
 
0.1%
Payment delayed 6 months 5
 
< 0.1%
Payment delayed 1 month 2
 
< 0.1%
Payment delayed 8 months 2
 
< 0.1%

Length

2024-11-04T10:14:15.537597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T10:14:15.630270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 20803
45.0%
payed 5687
 
12.3%
duly 5687
 
12.3%
payment 3510
 
7.6%
delayed 3510
 
7.6%
months 3508
 
7.6%
2 3159
 
6.8%
3 180
 
0.4%
4 69
 
0.1%
7 58
 
0.1%
Other values (5) 46
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 69429
24.2%
o 24313
 
8.5%
U 20803
 
7.3%
k 20803
 
7.3%
w 20803
 
7.3%
y 18394
 
6.4%
d 18394
 
6.4%
16217
 
5.7%
e 16217
 
5.7%
a 12707
 
4.4%
Other values (15) 48649
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 286729
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 69429
24.2%
o 24313
 
8.5%
U 20803
 
7.3%
k 20803
 
7.3%
w 20803
 
7.3%
y 18394
 
6.4%
d 18394
 
6.4%
16217
 
5.7%
e 16217
 
5.7%
a 12707
 
4.4%
Other values (15) 48649
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 286729
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 69429
24.2%
o 24313
 
8.5%
U 20803
 
7.3%
k 20803
 
7.3%
w 20803
 
7.3%
y 18394
 
6.4%
d 18394
 
6.4%
16217
 
5.7%
e 16217
 
5.7%
a 12707
 
4.4%
Other values (15) 48649
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 286729
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 69429
24.2%
o 24313
 
8.5%
U 20803
 
7.3%
k 20803
 
7.3%
w 20803
 
7.3%
y 18394
 
6.4%
d 18394
 
6.4%
16217
 
5.7%
e 16217
 
5.7%
a 12707
 
4.4%
Other values (15) 48649
17.0%

payment_status_may
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
Unknown
21493 
Payed duly
5539 
Payment delayed 2 months
2626 
Payment delayed 3 months
 
178
Payment delayed 4 months
 
84
Other values (4)
 
80

Length

Max length24
Median length7
Mean length9.2357667
Min length7

Characters and Unicode

Total characters277073
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown 21493
71.6%
Payed duly 5539
 
18.5%
Payment delayed 2 months 2626
 
8.8%
Payment delayed 3 months 178
 
0.6%
Payment delayed 4 months 84
 
0.3%
Payment delayed 7 months 58
 
0.2%
Payment delayed 5 months 17
 
0.1%
Payment delayed 6 months 4
 
< 0.1%
Payment delayed 8 months 1
 
< 0.1%

Length

2024-11-04T10:14:15.988606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T10:14:16.081486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 21493
48.4%
payed 5539
 
12.5%
duly 5539
 
12.5%
payment 2968
 
6.7%
delayed 2968
 
6.7%
months 2968
 
6.7%
2 2626
 
5.9%
3 178
 
0.4%
4 84
 
0.2%
7 58
 
0.1%
Other values (3) 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 70415
25.4%
o 24461
 
8.8%
U 21493
 
7.8%
k 21493
 
7.8%
w 21493
 
7.8%
y 17014
 
6.1%
d 17014
 
6.1%
e 14443
 
5.2%
14443
 
5.2%
a 11475
 
4.1%
Other values (14) 43329
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 277073
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 70415
25.4%
o 24461
 
8.8%
U 21493
 
7.8%
k 21493
 
7.8%
w 21493
 
7.8%
y 17014
 
6.1%
d 17014
 
6.1%
e 14443
 
5.2%
14443
 
5.2%
a 11475
 
4.1%
Other values (14) 43329
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 277073
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 70415
25.4%
o 24461
 
8.8%
U 21493
 
7.8%
k 21493
 
7.8%
w 21493
 
7.8%
y 17014
 
6.1%
d 17014
 
6.1%
e 14443
 
5.2%
14443
 
5.2%
a 11475
 
4.1%
Other values (14) 43329
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 277073
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 70415
25.4%
o 24461
 
8.8%
U 21493
 
7.8%
k 21493
 
7.8%
w 21493
 
7.8%
y 17014
 
6.1%
d 17014
 
6.1%
e 14443
 
5.2%
14443
 
5.2%
a 11475
 
4.1%
Other values (14) 43329
15.6%

payment_status_apr
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
Unknown
21181 
Payed duly
5740 
Payment delayed 2 months
2766 
Payment delayed 3 months
 
184
Payment delayed 4 months
 
49
Other values (4)
 
80

Length

Max length24
Median length7
Mean length9.3187667
Min length7

Characters and Unicode

Total characters279563
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowPayment delayed 2 months
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown 21181
70.6%
Payed duly 5740
 
19.1%
Payment delayed 2 months 2766
 
9.2%
Payment delayed 3 months 184
 
0.6%
Payment delayed 4 months 49
 
0.2%
Payment delayed 7 months 46
 
0.2%
Payment delayed 6 months 19
 
0.1%
Payment delayed 5 months 13
 
< 0.1%
Payment delayed 8 months 2
 
< 0.1%

Length

2024-11-04T10:14:16.192118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T10:14:16.288392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 21181
47.1%
payed 5740
 
12.8%
duly 5740
 
12.8%
payment 3079
 
6.8%
delayed 3079
 
6.8%
months 3079
 
6.8%
2 2766
 
6.1%
3 184
 
0.4%
4 49
 
0.1%
7 46
 
0.1%
Other values (3) 34
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 69701
24.9%
o 24260
 
8.7%
U 21181
 
7.6%
k 21181
 
7.6%
w 21181
 
7.6%
y 17638
 
6.3%
d 17638
 
6.3%
e 14977
 
5.4%
14977
 
5.4%
a 11898
 
4.3%
Other values (14) 44931
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 279563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 69701
24.9%
o 24260
 
8.7%
U 21181
 
7.6%
k 21181
 
7.6%
w 21181
 
7.6%
y 17638
 
6.3%
d 17638
 
6.3%
e 14977
 
5.4%
14977
 
5.4%
a 11898
 
4.3%
Other values (14) 44931
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 279563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 69701
24.9%
o 24260
 
8.7%
U 21181
 
7.6%
k 21181
 
7.6%
w 21181
 
7.6%
y 17638
 
6.3%
d 17638
 
6.3%
e 14977
 
5.4%
14977
 
5.4%
a 11898
 
4.3%
Other values (14) 44931
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 279563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 69701
24.9%
o 24260
 
8.7%
U 21181
 
7.6%
k 21181
 
7.6%
w 21181
 
7.6%
y 17638
 
6.3%
d 17638
 
6.3%
e 14977
 
5.4%
14977
 
5.4%
a 11898
 
4.3%
Other values (14) 44931
16.1%

bill_statement_sep
Real number (ℝ)

High correlation  Zeros 

Distinct22723
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51223.331
Minimum-165580
Maximum964511
Zeros2008
Zeros (%)6.7%
Negative590
Negative (%)2.0%
Memory size234.5 KiB
2024-11-04T10:14:16.402001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13558.75
median22381.5
Q367091
95-th percentile201203.05
Maximum964511
Range1130091
Interquartile range (IQR)63532.25

Descriptive statistics

Standard deviation73635.861
Coefficient of variation (CV)1.4375453
Kurtosis9.8062893
Mean51223.331
Median Absolute Deviation (MAD)21800.5
Skewness2.663861
Sum1.5366999 × 109
Variance5.42224 × 109
MonotonicityNot monotonic
2024-11-04T10:14:16.512162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2008
 
6.7%
390 244
 
0.8%
780 76
 
0.3%
326 72
 
0.2%
316 63
 
0.2%
2500 59
 
0.2%
396 49
 
0.2%
2400 39
 
0.1%
416 29
 
0.1%
500 25
 
0.1%
Other values (22713) 27336
91.1%
ValueCountFrequency (%)
-165580 1
< 0.1%
-154973 1
< 0.1%
-15308 1
< 0.1%
-14386 1
< 0.1%
-11545 1
< 0.1%
-10682 1
< 0.1%
-9802 1
< 0.1%
-9095 1
< 0.1%
-8187 1
< 0.1%
-7438 1
< 0.1%
ValueCountFrequency (%)
964511 1
< 0.1%
746814 1
< 0.1%
653062 1
< 0.1%
630458 1
< 0.1%
626648 1
< 0.1%
621749 1
< 0.1%
613860 1
< 0.1%
610723 1
< 0.1%
608594 1
< 0.1%
604019 1
< 0.1%

bill_statement_aug
Real number (ℝ)

High correlation  Zeros 

Distinct22346
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49179.075
Minimum-69777
Maximum983931
Zeros2506
Zeros (%)8.4%
Negative669
Negative (%)2.2%
Memory size234.5 KiB
2024-11-04T10:14:16.621538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q12984.75
median21200
Q364006.25
95-th percentile194792.2
Maximum983931
Range1053708
Interquartile range (IQR)61021.5

Descriptive statistics

Standard deviation71173.769
Coefficient of variation (CV)1.4472368
Kurtosis10.302946
Mean49179.075
Median Absolute Deviation (MAD)20810
Skewness2.7052209
Sum1.4753723 × 109
Variance5.0657054 × 109
MonotonicityNot monotonic
2024-11-04T10:14:16.731022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2506
 
8.4%
390 231
 
0.8%
326 75
 
0.2%
780 75
 
0.2%
316 72
 
0.2%
396 51
 
0.2%
2500 51
 
0.2%
2400 42
 
0.1%
-200 29
 
0.1%
416 28
 
0.1%
Other values (22336) 26840
89.5%
ValueCountFrequency (%)
-69777 1
< 0.1%
-67526 1
< 0.1%
-33350 1
< 0.1%
-30000 1
< 0.1%
-26214 1
< 0.1%
-24704 1
< 0.1%
-24702 1
< 0.1%
-22960 1
< 0.1%
-18618 1
< 0.1%
-18088 1
< 0.1%
ValueCountFrequency (%)
983931 1
< 0.1%
743970 1
< 0.1%
671563 1
< 0.1%
646770 1
< 0.1%
624475 1
< 0.1%
605943 1
< 0.1%
597793 1
< 0.1%
586825 1
< 0.1%
581775 1
< 0.1%
577681 1
< 0.1%

bill_statement_jul
Real number (ℝ)

High correlation  Zeros 

Distinct22026
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47013.155
Minimum-157264
Maximum1664089
Zeros2870
Zeros (%)9.6%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2024-11-04T10:14:16.841914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12666.25
median20088.5
Q360164.75
95-th percentile187821.05
Maximum1664089
Range1821353
Interquartile range (IQR)57498.5

Descriptive statistics

Standard deviation69349.387
Coefficient of variation (CV)1.475106
Kurtosis19.783255
Mean47013.155
Median Absolute Deviation (MAD)19708.5
Skewness3.08783
Sum1.4103946 × 109
Variance4.8093375 × 109
MonotonicityNot monotonic
2024-11-04T10:14:16.952430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2870
 
9.6%
390 275
 
0.9%
780 74
 
0.2%
326 63
 
0.2%
316 62
 
0.2%
396 48
 
0.2%
2500 40
 
0.1%
2400 39
 
0.1%
416 29
 
0.1%
200 27
 
0.1%
Other values (22016) 26473
88.2%
ValueCountFrequency (%)
-157264 1
< 0.1%
-61506 1
< 0.1%
-46127 1
< 0.1%
-34041 1
< 0.1%
-25443 1
< 0.1%
-24702 1
< 0.1%
-20320 1
< 0.1%
-17706 1
< 0.1%
-15910 1
< 0.1%
-15641 1
< 0.1%
ValueCountFrequency (%)
1664089 1
< 0.1%
855086 1
< 0.1%
693131 1
< 0.1%
689643 1
< 0.1%
689627 1
< 0.1%
632041 1
< 0.1%
597415 1
< 0.1%
578971 1
< 0.1%
577957 1
< 0.1%
577015 1
< 0.1%

bill_statement_jun
Real number (ℝ)

High correlation  Zeros 

Distinct21548
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43262.949
Minimum-170000
Maximum891586
Zeros3195
Zeros (%)10.7%
Negative675
Negative (%)2.2%
Memory size234.5 KiB
2024-11-04T10:14:17.064113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-170000
5-th percentile0
Q12326.75
median19052
Q354506
95-th percentile174333.35
Maximum891586
Range1061586
Interquartile range (IQR)52179.25

Descriptive statistics

Standard deviation64332.856
Coefficient of variation (CV)1.4870197
Kurtosis11.309325
Mean43262.949
Median Absolute Deviation (MAD)18656
Skewness2.8219653
Sum1.2978885 × 109
Variance4.1387164 × 109
MonotonicityNot monotonic
2024-11-04T10:14:17.172120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3195
 
10.7%
390 246
 
0.8%
780 101
 
0.3%
316 68
 
0.2%
326 62
 
0.2%
396 44
 
0.1%
2400 39
 
0.1%
150 39
 
0.1%
2500 34
 
0.1%
1000 33
 
0.1%
Other values (21538) 26139
87.1%
ValueCountFrequency (%)
-170000 1
< 0.1%
-81334 1
< 0.1%
-65167 1
< 0.1%
-50616 1
< 0.1%
-46627 1
< 0.1%
-34503 1
< 0.1%
-27490 1
< 0.1%
-24303 1
< 0.1%
-22108 1
< 0.1%
-20320 1
< 0.1%
ValueCountFrequency (%)
891586 1
< 0.1%
706864 1
< 0.1%
628699 1
< 0.1%
616836 1
< 0.1%
572805 1
< 0.1%
569034 1
< 0.1%
565669 1
< 0.1%
563543 1
< 0.1%
548020 1
< 0.1%
542653 1
< 0.1%

bill_statement_may
Real number (ℝ)

High correlation  Zeros 

Distinct21010
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40311.401
Minimum-81334
Maximum927171
Zeros3506
Zeros (%)11.7%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2024-11-04T10:14:17.279385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-81334
5-th percentile0
Q11763
median18104.5
Q350190.5
95-th percentile165794.3
Maximum927171
Range1008505
Interquartile range (IQR)48427.5

Descriptive statistics

Standard deviation60797.156
Coefficient of variation (CV)1.5081876
Kurtosis12.305881
Mean40311.401
Median Absolute Deviation (MAD)17688.5
Skewness2.8763799
Sum1.209342 × 109
Variance3.6962941 × 109
MonotonicityNot monotonic
2024-11-04T10:14:17.383148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3506
 
11.7%
390 235
 
0.8%
780 94
 
0.3%
316 79
 
0.3%
326 62
 
0.2%
150 58
 
0.2%
396 47
 
0.2%
2400 39
 
0.1%
2500 37
 
0.1%
416 36
 
0.1%
Other values (21000) 25807
86.0%
ValueCountFrequency (%)
-81334 1
< 0.1%
-61372 1
< 0.1%
-53007 1
< 0.1%
-46627 1
< 0.1%
-37594 1
< 0.1%
-36156 1
< 0.1%
-30481 1
< 0.1%
-28335 1
< 0.1%
-23003 1
< 0.1%
-20753 1
< 0.1%
ValueCountFrequency (%)
927171 1
< 0.1%
823540 1
< 0.1%
587067 1
< 0.1%
551702 1
< 0.1%
547880 1
< 0.1%
530672 1
< 0.1%
524315 1
< 0.1%
516139 1
< 0.1%
514114 1
< 0.1%
508213 1
< 0.1%

bill_statement_apr
Real number (ℝ)

High correlation  Zeros 

Distinct20604
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38871.76
Minimum-339603
Maximum961664
Zeros4020
Zeros (%)13.4%
Negative688
Negative (%)2.3%
Memory size234.5 KiB
2024-11-04T10:14:17.486331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11256
median17071
Q349198.25
95-th percentile161912
Maximum961664
Range1301267
Interquartile range (IQR)47942.25

Descriptive statistics

Standard deviation59554.108
Coefficient of variation (CV)1.5320661
Kurtosis12.270705
Mean38871.76
Median Absolute Deviation (MAD)16755
Skewness2.8466446
Sum1.1661528 × 109
Variance3.5466917 × 109
MonotonicityNot monotonic
2024-11-04T10:14:17.600596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4020
 
13.4%
390 207
 
0.7%
780 86
 
0.3%
150 78
 
0.3%
316 77
 
0.3%
326 56
 
0.2%
396 45
 
0.1%
416 36
 
0.1%
-18 33
 
0.1%
2400 32
 
0.1%
Other values (20594) 25330
84.4%
ValueCountFrequency (%)
-339603 1
< 0.1%
-209051 1
< 0.1%
-150953 1
< 0.1%
-94625 1
< 0.1%
-73895 1
< 0.1%
-57060 1
< 0.1%
-51443 1
< 0.1%
-51183 1
< 0.1%
-46627 1
< 0.1%
-45734 1
< 0.1%
ValueCountFrequency (%)
961664 1
< 0.1%
699944 1
< 0.1%
568638 1
< 0.1%
527711 1
< 0.1%
527566 1
< 0.1%
514975 1
< 0.1%
513798 1
< 0.1%
511905 1
< 0.1%
501370 1
< 0.1%
499100 1
< 0.1%

previous_payment_sep
Real number (ℝ)

High correlation  Zeros 

Distinct7943
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5663.5805
Minimum0
Maximum873552
Zeros5249
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2024-11-04T10:14:17.706906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2100
Q35006
95-th percentile18428.2
Maximum873552
Range873552
Interquartile range (IQR)4006

Descriptive statistics

Standard deviation16563.28
Coefficient of variation (CV)2.9245246
Kurtosis415.25474
Mean5663.5805
Median Absolute Deviation (MAD)1932
Skewness14.668364
Sum1.6990742 × 108
Variance2.7434226 × 108
MonotonicityNot monotonic
2024-11-04T10:14:17.807689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5249
 
17.5%
2000 1363
 
4.5%
3000 891
 
3.0%
5000 698
 
2.3%
1500 507
 
1.7%
4000 426
 
1.4%
10000 401
 
1.3%
1000 365
 
1.2%
2500 298
 
1.0%
6000 294
 
1.0%
Other values (7933) 19508
65.0%
ValueCountFrequency (%)
0 5249
17.5%
1 9
 
< 0.1%
2 14
 
< 0.1%
3 15
 
0.1%
4 18
 
0.1%
5 12
 
< 0.1%
6 15
 
0.1%
7 9
 
< 0.1%
8 8
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
873552 1
< 0.1%
505000 1
< 0.1%
493358 1
< 0.1%
423903 1
< 0.1%
405016 1
< 0.1%
368199 1
< 0.1%
323014 1
< 0.1%
304815 1
< 0.1%
302000 1
< 0.1%
300039 1
< 0.1%

previous_payment_aug
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct7899
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5921.1635
Minimum0
Maximum1684259
Zeros5396
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2024-11-04T10:14:17.906756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1833
median2009
Q35000
95-th percentile19004.35
Maximum1684259
Range1684259
Interquartile range (IQR)4167

Descriptive statistics

Standard deviation23040.87
Coefficient of variation (CV)3.8912741
Kurtosis1641.6319
Mean5921.1635
Median Absolute Deviation (MAD)1991
Skewness30.453817
Sum1.776349 × 108
Variance5.3088171 × 108
MonotonicityNot monotonic
2024-11-04T10:14:18.016680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5396
 
18.0%
2000 1290
 
4.3%
3000 857
 
2.9%
5000 717
 
2.4%
1000 594
 
2.0%
1500 521
 
1.7%
4000 410
 
1.4%
10000 318
 
1.1%
6000 283
 
0.9%
2500 251
 
0.8%
Other values (7889) 19363
64.5%
ValueCountFrequency (%)
0 5396
18.0%
1 15
 
0.1%
2 20
 
0.1%
3 18
 
0.1%
4 11
 
< 0.1%
5 25
 
0.1%
6 8
 
< 0.1%
7 12
 
< 0.1%
8 9
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
1684259 1
< 0.1%
1227082 1
< 0.1%
1215471 1
< 0.1%
1024516 1
< 0.1%
580464 1
< 0.1%
415552 1
< 0.1%
401003 1
< 0.1%
388126 1
< 0.1%
385228 1
< 0.1%
384986 1
< 0.1%

previous_payment_jul
Real number (ℝ)

High correlation  Zeros 

Distinct7518
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.6815
Minimum0
Maximum896040
Zeros5968
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2024-11-04T10:14:18.126014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1800
Q34505
95-th percentile17589.4
Maximum896040
Range896040
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation17606.961
Coefficient of variation (CV)3.3693139
Kurtosis564.31123
Mean5225.6815
Median Absolute Deviation (MAD)1795
Skewness17.216635
Sum1.5677044 × 108
Variance3.1000509 × 108
MonotonicityNot monotonic
2024-11-04T10:14:18.227778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5968
 
19.9%
2000 1285
 
4.3%
1000 1103
 
3.7%
3000 870
 
2.9%
5000 721
 
2.4%
1500 490
 
1.6%
4000 381
 
1.3%
10000 312
 
1.0%
1200 243
 
0.8%
6000 241
 
0.8%
Other values (7508) 18386
61.3%
ValueCountFrequency (%)
0 5968
19.9%
1 13
 
< 0.1%
2 19
 
0.1%
3 14
 
< 0.1%
4 15
 
0.1%
5 18
 
0.1%
6 14
 
< 0.1%
7 18
 
0.1%
8 10
 
< 0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
896040 1
< 0.1%
889043 1
< 0.1%
508229 1
< 0.1%
417588 1
< 0.1%
400972 1
< 0.1%
397092 1
< 0.1%
380478 1
< 0.1%
371718 1
< 0.1%
349395 1
< 0.1%
344261 1
< 0.1%

previous_payment_jun
Real number (ℝ)

High correlation  Zeros 

Distinct6937
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4826.0769
Minimum0
Maximum621000
Zeros6408
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2024-11-04T10:14:18.330234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1296
median1500
Q34013.25
95-th percentile16014.95
Maximum621000
Range621000
Interquartile range (IQR)3717.25

Descriptive statistics

Standard deviation15666.16
Coefficient of variation (CV)3.246148
Kurtosis277.33377
Mean4826.0769
Median Absolute Deviation (MAD)1500
Skewness12.904985
Sum1.4478231 × 108
Variance2.4542856 × 108
MonotonicityNot monotonic
2024-11-04T10:14:18.438494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6408
 
21.4%
1000 1394
 
4.6%
2000 1214
 
4.0%
3000 887
 
3.0%
5000 810
 
2.7%
1500 441
 
1.5%
4000 402
 
1.3%
10000 341
 
1.1%
2500 259
 
0.9%
500 258
 
0.9%
Other values (6927) 17586
58.6%
ValueCountFrequency (%)
0 6408
21.4%
1 22
 
0.1%
2 22
 
0.1%
3 13
 
< 0.1%
4 20
 
0.1%
5 12
 
< 0.1%
6 16
 
0.1%
7 11
 
< 0.1%
8 7
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
621000 1
< 0.1%
528897 1
< 0.1%
497000 1
< 0.1%
432130 1
< 0.1%
400046 1
< 0.1%
331788 1
< 0.1%
330982 1
< 0.1%
320008 1
< 0.1%
313094 1
< 0.1%
292962 1
< 0.1%

previous_payment_may
Real number (ℝ)

High correlation  Zeros 

Distinct6897
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4799.3876
Minimum0
Maximum426529
Zeros6703
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2024-11-04T10:14:18.545870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1252.5
median1500
Q34031.5
95-th percentile16000
Maximum426529
Range426529
Interquartile range (IQR)3779

Descriptive statistics

Standard deviation15278.306
Coefficient of variation (CV)3.1833865
Kurtosis180.06394
Mean4799.3876
Median Absolute Deviation (MAD)1500
Skewness11.127417
Sum1.4398163 × 108
Variance2.3342662 × 108
MonotonicityNot monotonic
2024-11-04T10:14:18.650836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6703
 
22.3%
1000 1340
 
4.5%
2000 1323
 
4.4%
3000 947
 
3.2%
5000 814
 
2.7%
1500 426
 
1.4%
4000 401
 
1.3%
10000 343
 
1.1%
500 250
 
0.8%
6000 247
 
0.8%
Other values (6887) 17206
57.4%
ValueCountFrequency (%)
0 6703
22.3%
1 21
 
0.1%
2 13
 
< 0.1%
3 13
 
< 0.1%
4 12
 
< 0.1%
5 9
 
< 0.1%
6 7
 
< 0.1%
7 9
 
< 0.1%
8 6
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
426529 1
< 0.1%
417990 1
< 0.1%
388071 1
< 0.1%
379267 1
< 0.1%
332000 1
< 0.1%
331788 1
< 0.1%
330982 1
< 0.1%
326889 1
< 0.1%
317077 1
< 0.1%
310135 1
< 0.1%

previous_payment_apr
Real number (ℝ)

High correlation  Zeros 

Distinct6939
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5215.5026
Minimum0
Maximum528666
Zeros7173
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2024-11-04T10:14:18.754684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1117.75
median1500
Q34000
95-th percentile17343.8
Maximum528666
Range528666
Interquartile range (IQR)3882.25

Descriptive statistics

Standard deviation17777.466
Coefficient of variation (CV)3.4085815
Kurtosis167.16143
Mean5215.5026
Median Absolute Deviation (MAD)1500
Skewness10.640727
Sum1.5646508 × 108
Variance3.1603829 × 108
MonotonicityNot monotonic
2024-11-04T10:14:18.864702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7173
23.9%
1000 1299
 
4.3%
2000 1295
 
4.3%
3000 914
 
3.0%
5000 808
 
2.7%
1500 439
 
1.5%
4000 411
 
1.4%
10000 356
 
1.2%
500 247
 
0.8%
6000 220
 
0.7%
Other values (6929) 16838
56.1%
ValueCountFrequency (%)
0 7173
23.9%
1 20
 
0.1%
2 9
 
< 0.1%
3 14
 
< 0.1%
4 12
 
< 0.1%
5 7
 
< 0.1%
6 6
 
< 0.1%
7 5
 
< 0.1%
8 6
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
528666 1
< 0.1%
527143 1
< 0.1%
443001 1
< 0.1%
422000 1
< 0.1%
403500 1
< 0.1%
377000 1
< 0.1%
372495 1
< 0.1%
351282 1
< 0.1%
345293 1
< 0.1%
308000 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
23364 
1
6636 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Length

2024-11-04T10:14:18.962325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T10:14:19.035070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Interactions

2024-11-04T10:14:12.266988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:56.606380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:58.060286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:59.660105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:01.120222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:02.178164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:03.309348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:04.364552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:05.580573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:06.705022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:07.768354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:08.905572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:10.136790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:11.234331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:12.341565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:56.679889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:58.178905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:59.771077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:01.194175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:02.255613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:03.384621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:04.437565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:05.658024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:06.778691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:07.863088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:08.979111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:10.214706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:11.308284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:12.417234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:56.754531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:58.296201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:59.875080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:01.272048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:02.334356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:03.459646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:04.511150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:05.736910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:06.853228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:07.950587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:09.053850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:10.293483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:11.382821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:12.494576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:56.831610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:58.441991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:59.975485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:01.357064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:02.418532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:03.537872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:04.748169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:05.816679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:06.930699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:08.032254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:09.130107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:10.373816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:11.458569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:12.573968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:56.905010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:58.563218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:00.061386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:01.430482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:02.507846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:03.612435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:04.822743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:05.894090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:07.004326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:08.110273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:09.202602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:10.451662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:11.532137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:12.664950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:56.985169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:58.691299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:00.166287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:01.510002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:02.594878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:03.693900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:04.902428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:05.977928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:07.084501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:08.193179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:09.282733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:10.534367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:11.610882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:12.739725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:57.069118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:58.808879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:00.252821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:01.583735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:02.671649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:03.766961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:04.974119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:06.056144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:07.155549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:08.270486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:09.354039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:10.610617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:11.683254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:12.811959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:57.158737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:58.921028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:00.340392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:01.654863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:02.746561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:03.838996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:05.042926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:06.131132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:07.249529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:08.345808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:09.423212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:10.684918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:11.752398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:12.893351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:57.288609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:59.048380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:00.447602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:01.733780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:02.829724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:03.920029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:05.124337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:06.213079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:07.329326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:08.428559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:09.501393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:10.767954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:11.831139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:12.965460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:57.412008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:59.158540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:00.533245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:01.804505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:02.905277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:03.991292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:05.211547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:06.288259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:07.398876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:08.503268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:09.769321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:10.841298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:11.901456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:13.047785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:57.548489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:59.281524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:00.789624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:01.885171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:02.987600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:04.072165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:05.291605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:06.371637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:07.477563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:08.584990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:09.847812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:10.935268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:11.980733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:13.119625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:57.681472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:59.356067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:00.878925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:01.956193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:03.067508image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:04.142472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:05.361030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:06.446626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:07.547482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:08.664650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:09.917636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:11.007643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:12.050833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:13.196575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:57.824199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:59.435671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:00.970720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:02.033023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:03.156095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:04.219359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:05.437062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:06.530588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:07.621769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:08.745399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:09.991963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:11.086568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:12.126431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:13.269545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:57.942736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:13:59.533235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:01.045161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:02.104282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:03.231665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:04.291263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:05.507587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:06.628375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:07.692111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:08.824871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:10.064076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:11.158938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T10:14:12.194595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-04T10:14:19.110807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
agebill_statement_aprbill_statement_augbill_statement_julbill_statement_junbill_statement_maybill_statement_sepdefault_payment_next_montheducationlimit_balmarriagepayment_status_aprpayment_status_augpayment_status_julpayment_status_junpayment_status_maypayment_status_sepprevious_payment_aprprevious_payment_augprevious_payment_julprevious_payment_junprevious_payment_mayprevious_payment_sepsex
age1.0000.0000.0010.002-0.003-0.0000.0010.0480.1570.1860.3510.0290.0290.0290.0250.0270.0290.0390.0440.0330.0410.0380.0330.091
bill_statement_apr0.0001.0000.7650.8040.8480.9020.7340.0220.0270.0880.0220.0820.0790.0780.0760.0770.0890.5290.4870.5190.5700.6660.4560.026
bill_statement_aug0.0010.7651.0000.9080.8480.8030.9110.0310.0580.0490.0180.1040.1250.1280.1160.1070.1350.4290.4980.4680.4610.4490.6360.033
bill_statement_jul0.0020.8040.9081.0000.9040.8490.8580.0000.0590.0610.0160.1360.1470.1510.1550.1430.1580.4580.6380.4920.4890.4770.5500.018
bill_statement_jun-0.0030.8480.8480.9041.0000.9030.8070.0190.0360.0730.0210.0990.0880.0900.0920.1040.0980.4810.5550.6340.5070.5040.5120.026
bill_statement_may-0.0000.9020.8030.8490.9031.0000.7690.0170.0570.0810.0210.1350.1180.1170.1200.1270.1260.5090.5150.5490.6470.5250.4830.021
bill_statement_sep0.0010.7340.9110.8580.8070.7691.0000.0310.0420.0540.0220.0810.0960.0930.0850.0830.1040.4100.4720.4410.4420.4250.5020.026
default_payment_next_month0.0480.0220.0310.0000.0190.0170.0311.0000.0730.1570.0290.2500.3390.2950.2790.2700.4230.0280.0130.0240.0220.0350.0270.040
education0.1570.0270.0580.0590.0360.0570.0420.0731.0000.1590.1340.0700.0920.0860.0770.0690.0900.0290.0000.0210.0000.0170.0000.026
limit_bal0.1860.0880.0490.0610.0730.0810.0540.1570.1591.0000.0780.0770.0900.0880.0840.0790.0800.3170.2780.2840.2830.2940.2720.074
marriage0.3510.0220.0180.0160.0210.0210.0220.0290.1340.0781.0000.0210.0250.0200.0250.0190.0260.0020.0240.0230.0350.0100.0340.029
payment_status_apr0.0290.0820.1040.1360.0990.1350.0810.2500.0700.0770.0211.0000.2660.3900.5160.6190.2570.0060.0000.0000.0000.0410.0000.040
payment_status_aug0.0290.0790.1250.1470.0880.1180.0960.3390.0920.0900.0250.2661.0000.5950.4400.3250.6340.0180.0240.0110.0000.0110.0230.050
payment_status_jul0.0290.0780.1280.1510.0900.1170.0930.2950.0860.0880.0200.3900.5951.0000.5860.4700.4870.0400.0220.0130.0000.0060.0000.047
payment_status_jun0.0250.0760.1160.1550.0920.1200.0850.2790.0770.0840.0250.5160.4400.5861.0000.6230.3750.0540.0000.0360.0080.0000.0000.048
payment_status_may0.0270.0770.1070.1430.1040.1270.0830.2700.0690.0790.0190.6190.3250.4700.6231.0000.3040.0000.0000.0000.0390.0070.0000.044
payment_status_sep0.0290.0890.1350.1580.0980.1260.1040.4230.0900.0800.0260.2570.6340.4870.3750.3041.0000.0250.0000.0070.0000.0250.0000.039
previous_payment_apr0.0390.5290.4290.4580.4810.5090.4100.0280.0290.3170.0020.0060.0180.0400.0540.0000.0251.0000.4910.5050.5470.5490.4550.013
previous_payment_aug0.0440.4870.4980.6380.5550.5150.4720.0130.0000.2780.0240.0000.0240.0220.0000.0000.0000.4911.0000.5160.5200.4970.5120.000
previous_payment_jul0.0330.5190.4680.4920.6340.5490.4410.0240.0210.2840.0230.0000.0110.0130.0360.0000.0070.5050.5161.0000.5160.5340.5190.013
previous_payment_jun0.0410.5700.4610.4890.5070.6470.4420.0220.0000.2830.0350.0000.0000.0000.0080.0390.0000.5470.5200.5161.0000.5340.4860.000
previous_payment_may0.0380.6660.4490.4770.5040.5250.4250.0350.0170.2940.0100.0410.0110.0060.0000.0070.0250.5490.4970.5340.5341.0000.4680.015
previous_payment_sep0.0330.4560.6360.5500.5120.4830.5020.0270.0000.2720.0340.0000.0230.0000.0000.0000.0000.4550.5120.5190.4860.4681.0000.000
sex0.0910.0260.0330.0180.0260.0210.0260.0400.0260.0740.0290.0400.0500.0470.0480.0440.0390.0130.0000.0130.0000.0150.0001.000

Missing values

2024-11-04T10:14:13.402880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-04T10:14:13.681957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-04T10:14:13.869077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

limit_balsexeducationmarriageagepayment_status_seppayment_status_augpayment_status_julpayment_status_junpayment_status_maypayment_status_aprbill_statement_sepbill_statement_augbill_statement_julbill_statement_junbill_statement_maybill_statement_aprprevious_payment_sepprevious_payment_augprevious_payment_julprevious_payment_junprevious_payment_mayprevious_payment_aprdefault_payment_next_month
020000FemaleUniversityMarried24.0Payment delayed 2 monthsPayment delayed 2 monthsPayed dulyPayed dulyUnknownUnknown39133102689000068900001
1120000FemaleUniversitySingle26.0Payed dulyPayment delayed 2 monthsUnknownUnknownUnknownPayment delayed 2 months2682172526823272345532610100010001000020001
290000FemaleUniversitySingle34.0UnknownUnknownUnknownUnknownUnknownUnknown2923914027135591433114948155491518150010001000100050000
350000FemaleUniversityMarried37.0UnknownUnknownUnknownUnknownUnknownUnknown4699048233492912831428959295472000201912001100106910000
450000MaleUniversityMarried57.0Payed dulyUnknownPayed dulyUnknownUnknownUnknown86175670358352094019146191312000366811000090006896790
550000MaleGraduate schoolSingle37.0UnknownUnknownUnknownUnknownUnknownUnknown64400570695760819394196192002425001815657100010008000
6500000MaleGraduate schoolSingle29.0UnknownUnknownUnknownUnknownUnknownUnknown3679654120234450075426534830034739445500040000380002023913750137700
7100000FemaleUniversitySingle23.0UnknownPayed dulyPayed dulyUnknownUnknownPayed duly11876380601221-1595673806010581168715420
8140000FemaleHigh schoolMarried28.0UnknownUnknownPayment delayed 2 monthsUnknownUnknownUnknown11285140961210812211117933719332904321000100010000
920000MaleHigh schoolSingle35.0UnknownUnknownUnknownUnknownPayed dulyPayed duly0000130071391200013007112200
limit_balsexeducationmarriageagepayment_status_seppayment_status_augpayment_status_julpayment_status_junpayment_status_maypayment_status_aprbill_statement_sepbill_statement_augbill_statement_julbill_statement_junbill_statement_maybill_statement_aprprevious_payment_sepprevious_payment_augprevious_payment_julprevious_payment_junprevious_payment_mayprevious_payment_aprdefault_payment_next_month
29990140000MaleUniversityMarried41.0UnknownUnknownUnknownUnknownUnknownUnknown13832513714213911013826249675461216000700042281505200020000
29991210000MaleUniversityMarried34.0Payment delayed 3 monthsPayment delayed 2 monthsPayment delayed 2 monthsPayment delayed 2 monthsPayment delayed 2 monthsPayment delayed 2 months2500250025002500250025000000001
2999210000MaleHigh schoolMarried43.0UnknownUnknownUnknownUnknownUnknownUnknown88021040000002000000000
29993100000MaleGraduate schoolSingle38.0UnknownPayed dulyPayed dulyUnknownUnknownUnknown30421427102996706266947355004200011178440003000200020000
2999480000MaleUniversitySingle34.0Payment delayed 2 monthsPayment delayed 2 monthsPayment delayed 2 monthsPayment delayed 2 monthsPayment delayed 2 monthsPayment delayed 2 months7255777708793847751982607811587000350007000040001
29995220000NaNHigh schoolMarried39.0UnknownUnknownUnknownUnknownUnknownUnknown18894819281520836588004312371598085002000050033047500010000
29996150000MaleHigh schoolSingle43.0Payed dulyPayed dulyPayed dulyPayed dulyUnknownUnknown168318283502897951900183735268998129000
2999730000MaleUniversitySingle37.0Payment delayed 4 monthsPayment delayed 3 monthsPayment delayed 2 monthsPayed dulyUnknownUnknown35653356275820878205821935700220004200200031001
2999880000MaleHigh schoolMarried41.0Payment delayed 1 monthPayed dulyUnknownUnknownUnknownPayed duly-16457837976304527741185548944859003409117819265296418041
2999950000MaleUniversityMarried46.0UnknownUnknownUnknownUnknownUnknownUnknown4792948905497643653532428153132078180014301000100010001

Duplicate rows

Most frequently occurring

limit_balsexeducationmarriageagepayment_status_seppayment_status_augpayment_status_julpayment_status_junpayment_status_maypayment_status_aprbill_statement_sepbill_statement_augbill_statement_julbill_statement_junbill_statement_maybill_statement_aprprevious_payment_sepprevious_payment_augprevious_payment_julprevious_payment_junprevious_payment_mayprevious_payment_aprdefault_payment_next_month# duplicates
020000MaleUniversitySingle24.0Payment delayed 2 monthsPayment delayed 2 monthsPayment delayed 4 monthsPayment delayed 4 monthsPayment delayed 4 monthsPayment delayed 4 months16501650165016501650165000000012
150000MaleUniversitySingle26.0Payment delayed 1 monthUnknownUnknownUnknownUnknownUnknown00000000000002
280000FemaleHigh schoolMarried42.0UnknownUnknownUnknownUnknownUnknownUnknown00000000000002
380000FemaleUniversityMarried31.0UnknownUnknownUnknownUnknownUnknownUnknown00000000000002
480000FemaleUniversitySingle25.0UnknownUnknownUnknownUnknownUnknownUnknown00000000000002
590000FemaleGraduate schoolSingle31.0Payment delayed 1 monthUnknownUnknownUnknownUnknownUnknown00000000000002
6100000FemaleUniversityMarried49.0Payment delayed 1 monthUnknownUnknownUnknownUnknownUnknown00000000000002
7110000FemaleGraduate schoolSingle31.0Payment delayed 1 monthUnknownUnknownUnknownUnknownUnknown00000000000002
8140000MaleGraduate schoolSingle29.0Payment delayed 1 monthUnknownUnknownUnknownUnknownUnknown00000000000002
9150000FemaleGraduate schoolMarried31.0Payment delayed 1 monthUnknownUnknownUnknownUnknownUnknown00000000000002